Book Image

Hands-On ROS for Robotics Programming

By : Bernardo Ronquillo Japón
Book Image

Hands-On ROS for Robotics Programming

By: Bernardo Ronquillo Japón

Overview of this book

Connecting a physical robot to a robot simulation using the Robot Operating System (ROS) infrastructure is one of the most common challenges faced by ROS engineers. With this book, you'll learn how to simulate a robot in a virtual environment and achieve desired behavior in equivalent real-world scenarios. This book starts with an introduction to GoPiGo3 and the sensors and actuators with which it is equipped. You'll then work with GoPiGo3's digital twin by creating a 3D model from scratch and running a simulation in ROS using Gazebo. Next, the book will show you how to use GoPiGo3 to build and run an autonomous mobile robot that is aware of its surroundings. Finally, you'll find out how a robot can learn tasks that have not been programmed in the code but are acquired by observing its environment. You'll even cover topics such as deep learning and reinforcement learning. By the end of this robot programming book, you'll be well-versed with the basics of building specific-purpose applications in robotics and developing highly intelligent autonomous robots from scratch.
Table of Contents (19 chapters)
1
Section 1: Physical Robot Assembly and Testing
5
Section 2: Robot Simulation with Gazebo
8
Section 3: Autonomous Navigation Using SLAM
13
Section 4: Adaptive Robot Behavior Using Machine Learning

Applying Machine Learning in Robotics

This chapter provides a hands-on introduction to machine learning (ML) in robotics. Although we assume that you have not yet worked in such a field, it will be helpful to have some background in statistics and data analytics. In any case, this chapter intends to be a gentle introduction to the topic, favoring intuition instead of complex mathematical formulations, and putting the focus on understanding the common concepts used in the field of ML.

Throughout this chapter, we will devote the discussion to such concepts by providing specific examples of robots. This is somewhat original because most references and books on ML give examples oriented to data science. Hence, as you become more familiar with robotics, it should be easier for you to understand the concepts this way.

With the explanations about deep learning, you will understand how...